Machine learning models are becoming more prevalent in radio applications. Such systems couple machine learning techniques with hardware radio components to rapidly convert a sampling of a single radio signal into useful information, such as information bits, human understandable labels, or other types of information.
However, such legacy systems have be configured to process all types of radio signals and radio signal parameters at the time of deployment, relying on human configuration of transforms, and cannot be easily optimized for changing signal or channel parameters.